Custom semantic search experience driven by an ontology

ABSTRACT

Techniques include updating a semantic search function with a custom ontology, the semantic search function initially supporting a separate ontology having been used to enrich a corpus. The custom ontology is used to augment input of a search query for the semantic search function, thereby providing a custom user experience for searching the corpus.

BACKGROUND

The present invention generally relates to computer systems, and morespecifically, to a custom semantic search experience driven by anontology.

Natural language processing (NLP) is concerned with the interactionsbetween computers and human (natural) languages and how computersprocess and analyze large amounts of natural language data. This naturallanguage data is sometimes referred to as a corpus or corpora. Inlinguistics, a corpus or text corpus is a language resource consistingof a large and structured set of texts. NLP processing can occur onlarge corpora resulting in many annotations associated with the corpora.Semantic search of a corpus denotes searching with meaning, asdistinguished from lexical search where the search engine looks forliteral matches of the query words or variants of them withoutunderstanding the overall meaning of the query. Semantic search seeks toimprove search accuracy by understanding the searcher's intent and thecontextual meaning of terms as they appear in the searchable dataspaceto generate more relevant results. Semantic search systems considervarious points including context of search, location, intent, variationof words, synonyms, generalized and specialized queries, conceptmatching, and natural language queries to provide relevant searchresults. Some regard semantic search as a set of techniques forretrieving knowledge from richly structured data sources likeontologies. An ontology encompasses a representation, formal naming, anddefinition of the categories, properties, and relations between theconcepts, data, and entities that substantiate one, many, or all domainsof discourse. More simply, an ontology is a way of showing theproperties of a subject area and how they are related, by defining a setof concepts and categories that represent the subject.

SUMMARY

Embodiments of the present invention are directed to a custom semanticsearch experience driven by an ontology. A non-limiting examplecomputer-implemented method includes updating a semantic search functionwith a custom ontology, the semantic search function initiallysupporting a separate ontology having been used to enrich a corpus. Themethod includes using the custom ontology to augment input of a searchquery for the semantic search function, thereby providing a custom userexperience for searching the corpus.

In addition to one or more of the features described above or below, oras an alternative, further embodiments could include where the customontology is different from the separate ontology.

In addition to one or more of the features described above or below, oras an alternative, further embodiments could include where the customontology is received from a user and is curated independently from theseparate ontology.

In addition to one or more of the features described above or below, oras an alternative, further embodiments could include where using thecustom ontology to augment the input of the search query for thesemantic search function comprises generating suggestions associatedwith the input of the search query.

In addition to one or more of the features described above or below, oras an alternative, further embodiments could include where the customuser experience for searching the corpus comprises generating thesuggestions using the custom ontology.

In addition to one or more of the features described above or below, oras an alternative, further embodiments could include where the customuser experience for searching the corpus comprises generating thesuggestions using the custom ontology and the separate ontology.

In addition to one or more of the features described above or below, oras an alternative, further embodiments could include where the customuser experience for searching the corpus comprises generating thesuggestions using the custom ontology while avoiding execution ofnatural langue processing (NLP) on the corpus with the custom ontology.

In addition to one or more of the features described above or below, oras an alternative, further embodiments could include where software isprovided as a service in a cloud environment for providing the customuser experience for searching the corpus using the custom ontology toaugment the input of the search query.

Other embodiments of the present invention implement features of theabove-described method in computer systems and computer programproducts.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a block diagram of an example computer system for use inconjunction with one or more embodiments of the present invention;

FIG. 2 depicts a block diagram of a system for replacing mappings withina semantic search application over a commonly enriched corpus inaccordance with one or more embodiments of the present invention;

FIGS. 3A and 3B together depict a flowchart of a process for a customsemantic search experience driven by the user's ontology which includesreplacing mappings within a semantic search application over a commonlyenriched corpus in accordance with one or more embodiments of thepresent invention;

FIG. 4 is a flowchart of a process for a custom semantic searchexperience driven by the user's ontology over a commonly enriched corpuscontinuing from, responsive to, and/or concurrent with the process inFIGS. 3A and 3B in accordance with one or more embodiments of thepresent invention;

FIG. 5 is a flowchart of a computer-implemented method employing a userontology to support a customized search experience over a corpus thatwas enriched with a different ontology in accordance with one or moreembodiments of the present invention;

FIG. 6 is a flowchart of a computer-implemented method for a customsemantic search experience driven by a user ontology in accordance withone or more embodiments of the present invention;

FIG. 7 depicts a block diagram of providing custom semantic suggestionsfrom a user ontology concurrent with user input for a search query inaccordance with one or more embodiments of the present invention;

FIG. 8 depicts a cloud computing environment according to one or moreembodiments of the present invention;

FIG. 9 depicts abstraction model layers according to one or moreembodiments of the present invention;

FIG. 10 depicts a system for semantic linkage qualification ofontologically related entities according to one or more embodiments ofthe present invention;

FIG. 11 depicts a block diagram representation of a parse tree for anexemplary passage according to one or more embodiments of the invention;and

FIG. 12 depicts a flow diagram of a method for semantic linkagequalification of ontologically related entities according to one or moreembodiments of the invention.

DETAILED DESCRIPTION

One or more embodiments of the present invention provide a technique ofemploying a user ontology for use in aiding a customized searchexperience over a corpus that was previously enriched with a differentontology. The user ontology is a custom ontology specific to the user asoppose to a public ontology commonly available with the corpus. As anexample, one or more embodiments mimic a custom enrichment searchexperience without the computational cost in terms of processors,memory, time, expense, etc., of constructing and running a customenrichment of the corpus.

Although it is recognized as challenge to provide an enriched corpusthat can meet the needs of a wide variety of consumers, this is becausecustom enrichment of the entire corpus is a costly endeavor whichrequires rerunning the natural language processing (NLP) processor overthe entire corpus. However, one or more embodiments deliver the benefitsof a customized semantic search experience using a commonly enrichedcorpus without requiring the costly endeavor of rerunning the NLPprocessor over the entire corpus using the custom ontology. As notedherein, one or more embodiments provide the integration of a customontology with a semantic search user experience (e.g., typeahead) over acorpus enriched with a separate ontology, thereby affordingusers/consumers the ability to view a commonly enriched corpus throughthe lens of the custom ontology of his/her choice.

Turning now to FIG. 1 , a computer system 100 is generally shown inaccordance with one or more embodiments of the invention. The computersystem 100 can be an electronic, computer framework comprising and/oremploying any number and combination of computing devices and networksutilizing various communication technologies, as described herein. Thecomputer system 100 can be easily scalable, extensible, and modular,with the ability to change to different services or reconfigure somefeatures independently of others. The computer system 100 may be, forexample, a server, desktop computer, laptop computer, tablet computer,or smartphone. In some examples, computer system 100 may be a cloudcomputing node. Computer system 100 may be described in the generalcontext of computer system executable instructions, such as programmodules, being executed by a computer system. Generally, program modulesmay include routines, programs, objects, components, logic, datastructures, and so on that perform particular tasks or implementparticular abstract data types. Computer system 100 may be practiced indistributed cloud computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed cloud computing environment, program modulesmay be located in both local and remote computer system storage mediaincluding memory storage devices.

As shown in FIG. 1 , the computer system 100 has one or more centralprocessing units (CPU(s)) 101 a, 101 b, 101 c, etc., (collectively orgenerically referred to as processor(s) 101). The processors 101 can bea single-core processor, multi-core processor, computing cluster, or anynumber of other configurations. The processors 101, also referred to asprocessing circuits, are coupled via a system bus 102 to a system memory103 and various other components. The system memory 103 can include aread only memory (ROM) 104 and a random access memory (RAM) 105. The ROM104 is coupled to the system bus 102 and may include a basicinput/output system (BIOS) or its successors like Unified ExtensibleFirmware Interface (UEFI), which controls certain basic functions of thecomputer system 100. The RAM is read-write memory coupled to the systembus 102 for use by the processors 101. The system memory 103 providestemporary memory space for operations of said instructions duringoperation. The system memory 103 can include random access memory (RAM),read only memory, flash memory, or any other suitable memory systems.

The computer system 100 comprises an input/output (I/O) adapter 106 anda communications adapter 107 coupled to the system bus 102. The I/Oadapter 106 may be a small computer system interface (SCSI) adapter thatcommunicates with a hard disk 108 and/or any other similar component.The I/O adapter 106 and the hard disk 108 are collectively referred toherein as a mass storage 110.

Software 111 for execution on the computer system 100 may be stored inthe mass storage 110. The mass storage 110 is an example of a tangiblestorage medium readable by the processors 101, where the software 111 isstored as instructions for execution by the processors 101 to cause thecomputer system 100 to operate, such as is described herein below withrespect to the various Figures. Examples of computer program product andthe execution of such instruction is discussed herein in more detail.The communications adapter 107 interconnects the system bus 102 with anetwork 112, which may be an outside network, enabling the computersystem 100 to communicate with other such systems. In one embodiment, aportion of the system memory 103 and the mass storage 110 collectivelystore an operating system, which may be any appropriate operating systemto coordinate the functions of the various components shown in FIG. 1 .

Additional input/output devices are shown as connected to the system bus102 via a display adapter 115 and an interface adapter 116. In oneembodiment, the adapters 106, 107, 115, and 116 may be connected to oneor more I/O buses that are connected to the system bus 102 via anintermediate bus bridge (not shown). A display 119 (e.g., a screen or adisplay monitor) is connected to the system bus 102 by the displayadapter 115, which may include a graphics controller to improve theperformance of graphics intensive applications and a video controller. Akeyboard 121, a mouse 122, a speaker 123, etc., can be interconnected tothe system bus 102 via the interface adapter 116, which may include, forexample, a Super I/O chip integrating multiple device adapters into asingle integrated circuit. Suitable I/O buses for connecting peripheraldevices such as hard disk controllers, network adapters, and graphicsadapters typically include common protocols, such as the PeripheralComponent Interconnect (PCI) and the Peripheral Component InterconnectExpress (PCIe). Thus, as configured in FIG. 1 , the computer system 100includes processing capability in the form of the processors 101, and,storage capability including the system memory 103 and the mass storage110, input means such as the keyboard 121 and the mouse 122, and outputcapability including the speaker 123 and the display 119.

In some embodiments, the communications adapter 107 can transmit datausing any suitable interface or protocol, such as the internet smallcomputer system interface, among others. The network 112 may be acellular network, a radio network, a wide area network (WAN), a localarea network (LAN), or the Internet, among others. An external computingdevice may connect to the computer system 100 through the network 112.In some examples, an external computing device may be an externalwebserver or a cloud computing node.

It is to be understood that the block diagram of FIG. 1 is not intendedto indicate that the computer system 100 is to include all of thecomponents shown in FIG. 1 . Rather, the computer system 100 can includeany appropriate fewer or additional components not illustrated in FIG. 1(e.g., additional memory components, embedded controllers, modules,additional network interfaces, etc.). Further, the embodiments describedherein with respect to computer system 100 may be implemented with anyappropriate logic, wherein the logic, as referred to herein, can includeany suitable hardware (e.g., a processor, an embedded controller, or anapplication specific integrated circuit, among others), software (e.g.,an application, among others), firmware, or any suitable combination ofhardware, software, and firmware, in various embodiments.

FIG. 2 is a block diagram of a system 200 for replacing mappings withina semantic search application over a commonly enriched corpus inaccordance with one or more embodiments of the present invention. FIG. 2depicts one or more computers systems 202 coupled to computer system220. Computer systems 202 can be representative of numerous computers ina datacenter servicing various users. Computer system 220 can berepresentative of numerous user computers requesting customized accessto resources on computer systems 202. Elements of computer system 100may be used in and/or integrated into computers system 202 and computersystem 220. FIGS. 3A and 3B illustrate a flowchart of a process 300 fora custom semantic search experience driven by the user's ontology whichincludes replacing mappings within a semantic search application over acommonly enriched corpus in accordance with one or more embodiments ofthe present invention. Process 300 in FIGS. 3A and 3B will be describedwith reference to FIG. 2 .

At block 302, software application 204 on computer system 202 isconfigured to receive a request 230 for a customized semantic searchfrom computer system 220. Computer system 220 is the system for theuser, who is may also be referred to as the customer, tenant, etc.Computer system 220 can communicate with computer systems 202 over awired and/or wireless network. Using computer system 220, the user caninterface directly with software application 204 of computer system 202and/or use a client application 222 to interface with softwareapplication 204. Software application 204 may be implemented as software111 executed on one or more processors 101, as discussed in FIG. 1 .Similarly, client application 222 may be implemented using software 111configured to execute on one or more processors 101. Client application222 may include cookies, plug-ins, etc., and client application 222 mayserve as a piece of computer software that accesses the customizedsemantic search service for corpus 260 made available by computer system202.

Corpus 260 on computer system 202 is available to the public forsemantic search in which one or more ontologies 240 are used for thesemantic search. Corpus 260 has been enriched by one or more naturallanguage processing (NLP) services 212 using one or more ontologies 240.Corpus 260 includes databases of numerous documents 208 and annotations210 about those documents 208. Corpus 260 may contain hundreds,thousands, and/or millions of documents, also referred to as “big data”.In accordance with one or more embodiments, the enormous size of corpus260 requires management, processing, and search by a machine (such ascomputer system 202), for example, using computer-executableinstructions, and corpus 260 could not be practically managed, stored,analyzed, and/or processed as discussed herein within the human mind.For corpus 260, NLP processing via one or more NLP services 212 usingannotators 250 has occurred on documents 208 resulting in annotations210 associated with the text of documents 208. NLP services 212 used oneor more ontologies 240 to generate annotations 210 thereby enrichingcorpus 260. Ontologies 240 represent one or more public ontologiescommonly available with corpus 260. To enrich corpus 260, NLP services212 are configured to index the documents 208, and while using the indexof documents 208 along with public ontologies 240, NLP services 212 areconfigured to find insights and relationships in the text of documents208 and output this information as annotations 210 (or metadata)associated with documents 208. Oftentimes, a semantic search applicationwill provide a public, multi-tenant, enriched corpus including anontology that maps out all the relationships between the NLP-extractedentities. “Public” with respect to the ontology means provided andavailable to all tenants. Embodiments of the invention enable differentusers (i.e., tenants) with the ability to provide their own ontologies,such that the users can each perform semantic searches based on theirontological view of the world (i.e., their own entities andrelationships therein). Although software applications 204 can include asemantic search application and are able to perform a semantic searchover the NLP enriched corpus 260 using public ontologies 240, softwareapplications 204 are also configured to perform a customized semanticsearch using a user ontology 224 in place of and/or in addition topublic ontologies 240. The user ontology 224 is a custom ontologyspecific/personal to the user of computer system 220 as oppose to thepublic ontology 240 commonly available with corpus 260. User ontology224 was curated independently from the public ontology 240 that wasleveraged for the NLP enrichment process. For example, public ontologies240 may include entities and the relationships between those entitiesfor medial information of general medicine. User ontology 224 mayinclude entities and the relationships between those entities formedical information on particular specializations and disciplines ofmedicine, such as internal medicine, pediatrics, immunology, cardiology,etc. The request 230 includes the user ontology 224, and the request 230may also include a search query concurrently with the user ontology 224and/or responsive to sending user ontology 224 to computer system 202such as after computer system 202 prompts the user to input the searchquery. The request 230 may include a unique identification (ID) such asa numeric ID, alphanumeric ID, a unique name, etc., which uniquelyidentifies corpus 260 from other corpora on computer systems 202.Software application 204 is configured to upload user ontology 224 to beassociated with corpus 260 identified by the unique identification. Assuch, a cloned copy of user ontology 224 is stored in memory 206 andshown with dashed lines. Software application 204 may include, beintegrated with, and/or call another software application tool to indexuser ontology 224, thereby generating user ontology index 226. The userontology index 226 is a listing of all text/words (i.e., surface forms)in user ontology 224 along with their associated locations within userontology 224. Each user desiring a custom semantic search experiencewill have his/her own user ontology index 226 correlating to his/her ownuser ontology 224. The user ontology index 226 is a database indexand/or other search index (i.e., Lucene or elastic search index) whichallow for quick look-up by a typeahead search function 232 (includingsoftware application 204). In one or more embodiments, the ontologyindex 226 can be a Lucene-style index that is searched using aLucene-style query. Lucene is an inverted full-text index. This meansthat it takes all the documents, splits them into words, and then buildsan index for each word. Since the index is an exact string-match, thequery can be very fast.

At block 304, software application 204 on computer system 202 isconfigured to update the typeahead search function 232 with userontology 224, particularly user ontology index 226. The typeahead searchfunction 232 is updated to use ontology index 226 in place of and/or inaddition to an index 241 of public ontologies 240. Software application204 may include, be integrated with, and/or call typeahead searchfunction/application 232. Typeahead search or simply typeahead, which isalso known as autocomplete or autosuggest, is a language prediction toolused to predict and provide suggestions for users as they type in asearch query using, for example, a search index such as user ontologysearch index 226. By updating the typeahead search function 232 withuser ontology 224 particularly the ontology index 226, as the user typeshis/her search query, software application 204 is configured toautocomplete and autosuggest terms and/or phrases based on entities andrelationships in user ontology 224 in place of entities andrelationships in public ontologies 240 and/or in addition to publicontologies 240. The suggested terms and/or phrases will be specific andunique to user ontology 224 each time the user begins entering a searchquery to search enriched public corpus 260 on computer system 202. Inone or more embodiments, FIG. 7 depicts a block diagram of softwareapplication 204 providing custom semantic suggestions from user ontology224 concurrent with user input for a search query, such that thesuggestions are displayed/rendered for display to the user as he/shetypes in the search query. In one or more embodiments, custom semanticsuggestions are provided from user ontology 224, along with non-customsemantic suggestions from public ontologies 240 having been used toenrich corpus 260; the non-custom semantic suggestions from publicontologies 240 would have been used exclusively if the custom semanticsearch experience were not supported.

Returning to FIGS. 3A and 3B, at block 306, software application 204 oncomputer system 202 is configured to check whether any entities andrelationships in user ontology 224 are congruent with entities andrelationships in public ontologies 240, as part of mapping. In one ormore embodiments, software application 204 is configured to find andidentify entities and relationships in user ontology 224 which matchentities and relationships in public ontologies 240. In one or moreembodiments, congruent entities and relationships in user ontology 224and public ontologies 240 can be found by using various techniques. Tofind congruent entities, common techniques to identify the degree oftext similarity between words and phrases may be employed by softwareapplication 204, such as cosine similarity, Euclidean distance, Jaccarddistance, word movers distance, etc., over a vector representation ofthe text (e.g., word embedding model). The entities being evaluated forsimilarity are the entities between the public/provided ontology and theuser-provided ontology. Some entities can be mapped between the twoontologies (i.e., the same concept exists in both), but some entities inthe user ontology may not be represented in the public/providedontology, thereby requiring additional entity detection to be performedin that instance based on the seed entity name(s) in the user ontologyand common concept expansion techniques performed therein to identifyword variations of this new entity that are not represented in thepublic/provided ontology or the supported NLP annotators behind thepublic/provided ontology. With regard to finding congruent relations(similar to finding congruent entities), relations in thepublic/provided ontology are mapped to the user ontology whereapplicable, but in cases where relations in the user-provided ontologyare not represented in the public/provided ontology, additional relationdetection may be used to support these new user-provided relationsemployed for semantic search over a corpus. To detect new relationsexpressed in the user-provided ontology, relation names are first brokendown into valid words or tokens, for example, “mayTreat” is split into“may treat” (2 words/tokens). This type of pre-processing is performedas necessary to arrive at a natural language phrase that can be used toevaluate against intervening parse tree nodes between co-occurringentities as detailed in FIG. 11 (herein). The ontology informs thesoftware application 204 as to which entities are eligible/applicablefor a given relation, so that software application 204 is not blindlymatching every co-occurring entity against every possible relationship,rather just the eligible candidate relations based on the co-occurringentities that have an expressed relationship within the ontology. Foreach of the matches found between entities and relationships in bothuser ontology 224 and public ontologies 240, software application 204 isconfigured to link/map these related entities and relationships inmapping 246 at block 308.

Once software application 204 determines that no more entities and/orrelationships in user ontology 224 are congruent with entities andrelationships in public ontologies 240, software application 204 oncomputer system 202 is configured to identify new entities and/orrelationships in user ontology 224, which are in need of detection inand/or which are not represented by existing entities and/orrelationships in public ontologies 240 at block 310. To find the newentities in user ontology 224 which were not previouslymatched/congruent to existing entities in public ontologies 240,software application 204 on computer system 202 is configured togenerate synonymous terms and phrases in both the entities in userontology 224 and the entities in public ontologies 240 at block 312. Atblock 314, software application 204 on computer system 202 is configuredto identify matches/congruences between new entities in user ontology224 and existing entities in public ontologies 240 using, for example,synonymous terms and phrases for entities in user ontology 224 andsynonymous terms and phrases for entities in public ontologies 240. Oncethe matches and/or congruences are found between entities in userontology 224 and public ontology 240, software application 204 isconfigured to link/map (new) entities in user ontology 224 to existingentities in public ontology 240 in mapping 246. Software application 204can include, use, and/or call a combination of various softwareapplication tools to identify and find matches and/or congruencesbetween entities in user ontology 224 and public ontology 240.

For example, software application 204 may include functionality ofand/or use one or more software application tools (such as, e.g.,WordNet®) having lexical databases of semantic relations between words.The software application tool links words into semantic relationsincluding synonyms, hyponyms, and meronyms. The synonyms can be groupedinto synsets with short definitions and usage examples. The softwareapplication tool can be a combination and extension of a dictionary andthesaurus. The software application tool can use automatic text analysisand artificial intelligence. Additionally, software application 204 mayinclude functionality of and/or use one or more software applicationtools for word embedding. Word embedding is the collective name for aset of language modeling and feature learning techniques in naturallanguage processing (NLP) where words or phrases from the vocabulary aremapped to vectors of real numbers. Word embedding may involvemathematical embedding from a space with many dimensions per word to acontinuous vector space with a much lower dimension. Methods to generatethis mapping include neural networks, dimensionality reduction on theword co-occurrence matrix, probabilistic models, explainable knowledgebase method, and explicit representation in terms of the context inwhich words appear.

At block 316, to find the new relationships in user ontology 224 whichwere not previously matched/congruent to existing relationships inpublic ontologies 240, software application 204 on computer system 202is configured to inspect each of the new relationships versus theexisting relationships using parse tree analysis, predicate frames,etc., in addition to using the software application tools discussedabove for lexical databases of semantic relations between words and wordembedding. At block 318, software application 204 on computer system 202is configured to identify matches/congruences between the newrelationships of user ontology 224 and existing relationships of publicontologies 240 and link/map the matched/congruent (new) relationships inuser ontology 224 to existing relationships in public ontology 240 inmapping 246. In addition to employing user ontology index 226, thetypeahead search function 232 is updated with and/or linked to mapping246 to take advantage of the matches/congruences in entities andrelationships between user ontology 224 and entities and relationshipsin public ontologies 240. Since the links and connections in mapping 246are associated with terms (i.e., entities and relationships) of userontology 224, this allows a seamless customized search experience overcorpus 260 for the user of computer system 220 based on his/her own userontology 224 in place of and/or in addition to public ontology 240.Further, software application 204 can utilize one or more portions ofannotations 210 when performing blocks 312, 314, 316, and 318. Accordingto one or more embodiments, all or part of one or more processes inblocks 312, 314, 316, and 318 may be performed using any part of theexamples discussed in FIGS. 10-12 below in order to findmatches/congruences between entities and/or relationships in userontology 224 and public ontology 240 for block 310 (e.g., for the newentities and/or relationships in user ontology 224 which are in need ofdetection in and/or which are not (initially) found to be represented byexisting entities and/or relationships in public ontology 240).

FIG. 4 is a flowchart of a process 400 for a custom semantic searchexperience driven by the user's ontology over a commonly enriched corpus260 which continues from, is responsive to, and/or concurrent withprocess 300 discussed in FIGS. 3A and 3B in accordance with one or moreembodiments of the present invention. Although not explicitly shown inFIG. 4 , one or more blocks in process 400 of FIG. 4 can besimultaneously and/or nearly simultaneously processed with one or moreblocks in FIGS. 3A and 3B. At block 402, software application 204 isconfigured to receive request 230 which can further include a searchquery (such as the search query depicted in FIG. 7 ) in addition toand/or after receiving other information such as user ontology 224discussed herein, where the request 230 is for custom semantic searchexperience to search public corpus 260 using user ontology 224. Thecorpus 260 has been enriched by a separate public ontology 240 differentfrom user ontology 224. As text of the search query is being entered bythe user of computer system 220, for example, using client application222 coupled to software application 204 and/or directly using softwareapplication 204, software application 204 using typeahead searchfunction 232 is configured to suggest terms and phrases to the user inaccordance with user ontology 224 and user ontology index 226 at block404. For example, FIG. 7 illustrates that software application 204 candisplay custom semantic suggestions from user ontology 224 to the usersolely and/or along with non-custom semantic suggestions from publicontologies 240. At block 406, software application 204 is configured togenerate search results 242 from corpus 260 based on the user inputsearch query in request 230. As depicted in FIG. 7 , softwareapplication 204 can utilize mapping 246 to map/link search terms in theuser search query corresponding to user ontology 224 back to publicontologies 240 when searching corpus 260, and/or software application204 can search for one or more search terms of user search query incorpus 260 without mapping back to public ontologies 240. The searchresults 242 from the semantic search of corpus 260 aredisplayed/rendered to the user and transmitted from computer system 202to the user on computer system 220.

As technical advantages and benefits, one or more embodiments mimic acustom enrichment search experience without the computational cost (interms of processors, memory, time, expense, etc.) of constructing andrunning a custom enrichment of the corpus which would include rerunningthe NLP service/NLP processor over the entire corpus. Therefore, one ormore embodiments offer a customized semantic search experience using thecommonly enriched corpus 260 by integrating the user (custom) ontology224 with a semantic search user experience (e.g., typeahead searchfunction 232) over corpus 260 having been previously enriched with theseparate public ontology 240, thereby affording users/consumers theability to view a commonly enriched corpus through the lens of thecustom ontology of his/her choice.

Further technical advantages and benefits allow multiple users to eachapply their own ontologies (e.g., although one user ontology 224 for aparticular user is illustrated in FIG. 2 , user ontology 224 isrepresentative of numerous custom ontologies for respective users inwhich each user can individually apply his/her own ontology to thecorpus 260 as discussed herein) to a public corpus 260 enriched by thecommon public ontology 240 for the purposes of users being able toconstruct their own semantic search queries based on the constructs theyhave defined in their own ontology. One or more embodiments allowmultiple users to apply one or more of their own ontologies for thepurpose of semantically searching a public corpus through their point ofview (ontology), thereby avoiding and not requiring a custom enrichedcorpus per ontology, which would be prohibitively computationalexpensive in terms of processors, memory, bandwidth, etc., and timeconsuming. By supporting multiple custom ontologies over the publicshared corpus 260, system 200 is configured to individually customizethe semantic search experience for each customer. In system 200, customuser ontologies are explicit, thereby being defined within the userontology itself rather than a query that mimics the association. One ormore embodiments provide the ability to customize the entities as wellas the associations (relations/relationships) between those entities,again explicitly through a custom ontology itself rather than mimickingthe behavior via a query.

FIG. 5 is a flowchart of a computer-implemented method 500 employing auser ontology for use in aiding a customized search experience over acorpus that was enriched with a different ontology in accordance withone or more embodiments of the present invention. At block 502, softwareapplication 204 is configured to integrate a custom ontology (e.g., userontology 224) into a semantic search function (e.g., typeahead searchfunction 232), the semantic search function being configured to performa semantic search over a corpus 260 enriched with a separate ontology240. At block 504, software application 204 is configured to execute thesemantic search function using the custom ontology (e.g., user ontology224) to perform the semantic search of the corpus 260. For example,software application 204 is configured to parse corpus 260 andsemantically search for terms in the search query from the user whileusing user ontology 224, without requiring additional NLP processing byNLP services 212 with user ontology 224. At block 506, softwareapplication 204 is configured to provide/generate search results 242from the semantic search of the corpus 260 based on user input (e.g.,from computer system 220) received by the semantic search function oncomputer system 202.

The semantic search function uses a typeahead search function 232associated with the custom ontology (e.g., user ontology 224). Thesemantic search function uses a typeahead search function 232 togenerate and display suggestions based on the custom ontology (e.g.,user ontology 224) as an alternative to the separate ontology 240. Thesemantic search function uses a typeahead search function 232 togenerate and display suggestions based on the custom ontology (e.g.,user ontology 224) in addition to the separate ontology 240.

The separate ontology 240 is used to explicitly enrich the corpus 260.Software application 204 is configured to index the custom ontology(e.g., user ontology 224). The semantic search function uses the userontology index 226 of the custom ontology (e.g., user ontology 224) togenerate suggestions for a user entering the input (via computer system220 into computer system 202) as a search query. Integrating the customontology (e.g., user ontology 224) into the semantic search function(e.g., typeahead search function 232) comprises determining congruences(which are linked/mapped in mapping 246) between entities andrelationships in the custom ontology and the separate ontology (e.g.,public ontology 240), the semantic search function (e.g., typeaheadsearch function 232) employing the congruences (via mapping 246) tosupport the input received by the semantic search function. Theintegrating and the executing enable unilaterally provisioning computingcapabilities for providing a customized search experience over thecorpus 260 that was enriched with the separate ontology 240 differentfrom the custom ontology (e.g., user ontology 224).

FIG. 6 is a flowchart of a computer-implemented method 600 a customsemantic search experience driven by an ontology in accordance with oneor more embodiments of the present invention. At block 602, softwareapplication 204 is configured to update a semantic search function(e.g., typeahead search function 232) with a custom ontology (e.g., userontology 224), the semantic search function initially supporting aseparate ontology (e.g., public ontology 240) having been used to enricha corpus 260. At block 604, software application 204 is configured touse the custom ontology (e.g., user ontology 224) to augment input of asearch query for the semantic search function, thereby providing acustom user experience for searching the corpus 260.

The custom ontology (e.g., user ontology 224) is different from theseparate ontology (e.g., public ontology 240). The custom ontology isreceived by computer system 202 from a user using computer system 220and is curated independently from the separate ontology. Using thecustom ontology to augment the input of the search query for thesemantic search function comprises generating suggestions associatedwith the input of the search query. The custom user experience forsearching the corpus 260 includes generating the suggestions using thecustom ontology (e.g., custom semantic suggestions using user ontology224). The custom user experience for searching the corpus 260 includesgenerating the suggestions using the custom ontology (e.g., customsemantic suggestions specific to user ontology 224) and the separateontology (e.g., non-custom semantic suggestions specific to publicontology 240). The custom user experience for searching the corpus 260includes generating the suggestions using the custom ontology whileavoiding performing/execution of natural langue processing (NLP) (viaNLP services 212) on the corpus 260 with the custom ontology (e.g., userontology 224). Software is provided as a service in a cloud environmentfor providing the custom user experience for searching the corpus 260using the custom ontology to augment the input of the search query.

One or more embodiments of the invention provide a relation annotatorthat produces relation annotations between co-occurring entities linkedwithin an ontology. This annotator evaluates a passage where twoontologically linked entities co-occur to determine whether there existany semantic linkages within the passage that are congruent with therelationship expressed within the ontology. That is to say, thesurrounding neighborhood within a passage, document, and the like areanalyzed to determine whether the ontological relation annotation can beconfirmed by the existing words and phrases in the surroundingneighborhood of the co-occurring entities. FIG. 10 depicts a blockdiagram of a system for semantic linkage qualification of ontologicallyrelated entities according to one or more embodiments of the presentinvention. It is expected that any new entities and/or relationships inuser ontology 224 will be detected and/or found to be represented byexisting entities and/or relationships in public ontology 240 asdiscussed above in FIG. 3 . Further, to assist with processes performedin blocks 312, 314, 316, and 318 such as, for example, when one or morenew entities and/or relationships in user ontology 224 may not have been(initially) detected and/or found to be represented by existing entitiesand/or relationships in public ontology 240, system 1000 may be utilizedas discussed herein. One or more software applications 204 on computersystem 202 can be utilized to execute and process functions/processesdiscussed in FIGS. 10-12 and/or call other software applications toexecute and process functions/processes discussed in FIGS. 10-12 .

Referring to FIG. 10 , the system 1000 includes a semantic linkageengine 1002 that is configured and operable to analyze a set of passages1006 (e.g., the passages 1006 include/correspond to the new entitiesand/or relationships in user ontology 224 which are in need of detectionin and/or which are not (initially) found to be represented by existingentities and/or relationships in public ontology 240; also, the set ofpassages 1006 include/correspond to passages (e.g., existing entitiesand/or relationships) in public ontology 240) and utilize either anexisting ontology 1020 or a defined ontology having ontologicalrelationship annotations for existing entities/concepts that are ofinterest. The ontology 1020 is an ontology different from user ontology224 and public ontology 240 but accessed by computer system 202. Theontology 1020 may be stored on and/or coupled to computer system 202.Computer system 202 may access ontology 1020 over a network such as theInternet and/or an intranet. The semantic linkage engine 1002 is furtherconfigured and operable to generate relationship annotations 1012 forco-occurring entities that exists in passages in the set of passages1006. These relationship annotations 1012 are generated as aconfirmation of the ontological relationship annotation from theontology 1020 after a semantic analysis is performed on the passage todetermine a congruency score between the ontological relationship andthe other words, phrases, entities, and concepts found in the passage.In one or more embodiments of the invention, the passages describedherein are natural language text and can vary in size and subjectmatter. For ease of description, the subject matter will be describedherein for usage in the medical field, but this is not intended to limitthe scope of the present invention to this field.

In one or more embodiments of the invention, pre-processing of the setof passages 1006 can occur prior to analysis by the semantic linkageengine 1002 utilizing a dictionary 1018 or set of dictionaries. Thispre-processing can include, but is not limited to, entity detectionwhich can identify and define entities/concepts that exist in the set ofpassages 1006 that are relevant. The entity detection can be performedutilizing techniques such as machine-learned or rule-based entitydetection annotators. The semantic linkage engine 1002 can automaticallyor through operation by a domain expert identify co-occurring entitieshaving an ontological relation defined by the ontology 1020 that are ofinterest to the domain expert. For example, co-occurring entities couldbe a diagnosis and an associated medication with an ontological relationbeing defined as treatment or prescription. As mentioned before, thepre-processing can perform entity detection to determine passages thathave the exemplary co-occurring entities. The other words and phrases inthe passage can be analyzed to determine a congruency for these wordsand phrases in the passage using semantic analysis. Semantic analysisrefers to measuring contextual similarity between words and phrases in apassage. The semantic analysis is performed by the semantic linkageengine 1002. During an analysis of a passage, the semantic linkageengine 1002 can determine a congruency score between an ontologicalrelation and the words and phrases in the passage. This congruency scorecan be compared to a pre-defined threshold to either confirm or rejectthe ontological relation taken from the ontology 1020. If confirmed(i.e., the congruency score exceeds the threshold), the semantic linkageengine 1002 can generate a relation annotation for the co-occurringentities in the passage and apply this relation annotation to thepassage.

Semantic analysis can include parsing rules. FIG. 11 depicts a blockdiagram representation of a parse tree for an exemplary passageaccording to one or more embodiments of the invention. In the parse tree1100 there is an exemplary passage that states, “Patient was prescribedcisplatin for treatment of her lung cancer.” The parse tree 1100 parsethe exemplary sentence into nodes representing either words or phrases(e.g., patient, lung cancer, etc.). The nodes are in a hierarchicalstructure and delineated by parts of speech (i.e., verb, noun,prepositional phrase, and determiner). In the exemplary passage, the twoco-occurring entities are cisplatin (entity 1) and lung cancer (entity2). The ontological relation for these two co-occurring entities can be“treats” and “prescribedfor”. The ontology may store these ontologicalrelations in the following format: <ENTITY1>-<RELATION>-<ENTITY2>. Withthat, the two co-occurring entities would show as Cisplatin-Treats-LungCancer and Cisplatin PrescribedFor-Lung Cancer. Not that PrescribedForis an ontological relation which can be further broken down into“Prescribed For” or simply “Prescribed.” For the exemplary passage, asemantic analysis can be performed to determine that the ontologicalrelations are congruent with the words and phrases of the passage. Thiscan be performed using a variety of techniques including, but notlimited to, any suitable vector formation and clustering technique torepresent each training/validation set phrase in vector form and thendetermine a similarity or grouping of different vectors, such as byusing a neural network language model representation techniques (e.g.,Word2Vec, Doc2Vec, or similar tool) to convert words and phrases tovectors which are then input to a clustering algorithm to place wordsand phrases with similar meanings close to each other in a Euclideanspace. The intervening nodes of the parse tree 1100 constitute a set oftokens or words that can then be matched against the relation name,which may constitute one or more other words. Relation names such as“mayTreat” can be pre-processed to isolate the unique tokens/wordstherein. Now we have two sets of text (1. Intervening tokens fromco-occurring entities in the passage) and (2. Tokens from the relationname), with which to analyze the degree of congruency or meaning.Techniques such as word movers distance, cosine similarity, and the likecan be employed to assess the degree of similarity between the two textexcerpts. Furthermore, stop words may be removed to reduce noise andpolarity may be explicitly factored in as a penalty to thescore—‘polarity’ in the sense that a token or word is negated in one setof text, but not the other (thus, the text may be highly similar, butthe presence of the term ‘no’ can drastically change the meaning).

In one or more embodiments of the invention, the semantic linkage engine1002 can be implemented on the processing system 100 found in FIG. 1 .The processing steps described with reference to the elements of FIG. 10can be performed utilizing the processing system 100 in FIG. 1 .Additionally, the cloud computing system 10 can be in wired or wirelesselectronic communication with one or all of the elements of the system1000. Cloud 50 (discussed below) can supplement, support or replace someor all of the functionality of the elements of the system 1000.Additionally, some or all of the functionality of the elements of system1000 can be implemented as a node 10 (shown in FIGS. 8 and 9 ) of cloud50. Cloud computing node 10 is only one example of a suitable cloudcomputing node and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the invention describedherein.

In embodiments of the invention, the semantic linkage engine 1002 canalso be implemented as so-called classifiers (described in more detailbelow). In one or more embodiments of the invention, the features of thevarious engines/classifiers (1002) described herein can be implementedon the processing system 100 shown in FIG. 1 , or can be implemented ona neural network (not shown). In embodiments of the invention, thefeatures of the engines/classifiers 1002 can be implemented byconfiguring and arranging the processing system 100 to execute machinelearning (ML) algorithms. In general, ML algorithms, in effect, extractfeatures from received data (e.g., inputs to the engines 1002) in orderto “classify” the received data. Examples of suitable classifiersinclude but are not limited to neural networks (described in greaterdetail below), support vector machines (SVMs), logistic regression,decision trees, hidden Markov Models (HMMs), etc. The end result of theclassifier's operations, i.e., the “classification,” is to predict aclass for the data. The ML algorithms apply machine learning techniquesto the received data in order to, over time, create/train/update aunique “model.” The learning or training performed by theengines/classifiers 1002 can be supervised, unsupervised, or a hybridthat includes aspects of supervised and unsupervised learning.Supervised learning is when training data is already available andclassified/labeled. Unsupervised learning is when training data is notclassified/labeled so must be developed through iterations of theclassifier. Unsupervised learning can utilize additionallearning/training methods including, for example, clustering, anomalydetection, neural networks, deep learning, and the like.

In embodiments of the invention where the engines/classifiers 1002 areimplemented as neural networks, a resistive switching device (RSD) canbe used as a connection (synapse) between a pre-neuron and apost-neuron, thus representing the connection weight in the form ofdevice resistance. Neuromorphic systems are interconnected processorelements that act as simulated “neurons” and exchange “messages” betweeneach other in the form of electronic signals. Similar to the so-called“plasticity” of synaptic neurotransmitter connections that carrymessages between biological neurons, the connections in neuromorphicsystems such as neural networks carry electronic messages betweensimulated neurons, which are provided with numeric weights thatcorrespond to the strength or weakness of a given connection. Theweights can be adjusted and tuned based on experience, makingneuromorphic systems adaptive to inputs and capable of learning. Forexample, a neuromorphic/neural network for handwriting recognition isdefined by a set of input neurons, which can be activated by the pixelsof an input image. After being weighted and transformed by a functiondetermined by the network's designer, the activations of these inputneurons are then passed to other downstream neurons, which are oftenreferred to as “hidden” neurons. This process is repeated until anoutput neuron is activated. Thus, the activated output neuron determines(or “learns”) which character was read. Multiple pre-neurons andpost-neurons can be connected through an array of RSD, which naturallyexpresses a fully-connected neural network. In the descriptions here,any functionality ascribed to the system 1000 can be implemented usingthe processing system 100 applies.

The semantic linkage engine 1002 can perform natural language processing(NLP) analysis techniques on the sets of passages 1006 which arecomposed of natural language text. NLP is utilized to derive meaningfrom natural language. The semantic linkage engine 1002 can analyze theset of passages 1006 by parsing, syntactical analysis, morphologicalanalysis, and other processes including statistical modeling andstatistical analysis. The type of NLP analysis can vary by language andother considerations. The NLP analysis is utilized to generate a firstset of NLP structures and/or features which can be utilized by thesemantic linkage engine 1002 to determine congruency between words andphrases in a passage. These NLP structures include a translation and/orinterpretation of the natural language input, including synonymousvariants thereof. The semantic linkage engine 1002 can analyze thefeatures to determine a context for the features. NLP analysis can beutilized to extract attributes (features) from the natural language.These extracted attributes can be analyzed by the semantic linkageengine 1002 to determine a congruency score and compare this score to apre-defined threshold to determine whether to generate a relationannotation for the passage being analyzed.

FIG. 12 depicts a flow diagram of a method for semantic linkagequalification of ontologically related entities according to one or moreembodiments of the invention. The method 1200 includes determining, by aprocessor, an ontology, the ontology comprising a plurality ofontological relationships, as shown in block 1202. Determining includesreceiving an ontology or creating an ontology that defines ontologicalrelationships between entities of interest to a domain expert. Theontological relationships are chosen to be easily associated with thesubject matter of the application of this method. For example, in themedical field, utilizing certain terms or jargon for the definedontological relationships in the ontology assists with applying it tonatural language passages being analyzed. The method 1200, at block1204, includes receiving, by a processor, a plurality of passages. Asnoted above, the plurality of passages can be natural language text of agiven subject matter or can be any natural language text depending onthe scope of the ontology. At block 1206 of the method 1200, the method1200 includes determining, by the processor, a target set ofco-occurring entities comprising a first entity and a second entity. Thetarget co-occurring entities can be determined by a domain expert thatis interested in these entities and looking to apply annotations forthese entities. Also, at block 1208, the method 1200 includesdetermining a first passage in the plurality of passages that includesthe first entity and the second entity. The first passage can be asentence, paragraph, and document based on the application. The method1200, at block 1210, includes determining, from the ontology, a firstontological relationship between the first entity and the second entity.Also, the method 1200, at block 1212, includes analyzing the firstpassage to determine a congruency score for the first ontologicalrelationship. And at block 1214, the method 1200 includes generating arelationship annotation between the first entity and the second entityin the first passages based on the congruency score being within athreshold.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 8 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described herein above, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 9 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 8 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 9 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and software applications (e.g., softwareapplications 204, typeahead search functions 232, and NLP services 212)implemented in workloads and functions 96. Also, software applicationscan function with and/or be integrated with Resource provisioning 81.

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

One or more of the methods described herein can be implemented with anyor a combination of the following technologies, which are each wellknown in the art: a discrete logic circuit(s) having logic gates forimplementing logic functions upon data signals, an application specificintegrated circuit (ASIC) having appropriate combinational logic gates,a programmable gate array(s) (PGA), a field programmable gate array(FPGA), etc.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

In some embodiments, various functions or acts can take place at a givenlocation and/or in connection with the operation of one or moreapparatuses or systems. In some embodiments, a portion of a givenfunction or act can be performed at a first device or location, and theremainder of the function or act can be performed at one or moreadditional devices or locations.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thepresent disclosure has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the disclosure. The embodiments were chosen and described in order tobest explain the principles of the disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the steps (or operations) described thereinwithout departing from the spirit of the disclosure. For instance, theactions can be performed in a differing order or actions can be added,deleted or modified. Also, the term “coupled” describes having a signalpath between two elements and does not imply a direct connection betweenthe elements with no intervening elements/connections therebetween. Allof these variations are considered a part of the present disclosure.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” are understood to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are understood to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection”can include both an indirect “connection” and a direct “connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a processor, a request from a user device, the requestcomprising a custom ontology as a structure having a plurality ofentities and relationships between the plurality of entities, whereinthe custom ontology having been received from the user device is to beassociated with a search query; in response to receiving the customontology transmitted from the user device, updating, by the processor, asemantic search function with the custom ontology and a custom ontologyindex in place of an original ontology, the semantic search functioninitially supporting the original ontology originally having been usedto enrich a corpus, the custom ontology being separate and distinct fromthe original ontology having enriched the corpus, wherein the corpuscomprises documents enriched by annotations in which the originalontology is used to generate the annotations, wherein natural languageprocessing uses the original ontology to generate the annotations suchthat the annotations are between co-occurring entities linked within theoriginal ontology, wherein the custom ontology index is configured toindex the custom ontology using a string match; in response to using thecustom ontology to replace the original ontology to augment input of thesearch query for the semantic search function to search the corpus,generating suggestions for the search query as a user is entering thesearch query by using the custom ontology and the custom ontology indexin place of the separate ontology for the suggestions such that thesuggestions displayed are from the custom ontology, thereby providing acustom user experience for searching the corpus; and searching thecorpus using the custom ontology, in response to the search query of thecustom ontology being mapped to the original ontology.
 2. Thecomputer-implemented method of claim 1, wherein the custom ontology isdifferent from the separate ontology.
 3. The computer-implemented methodof claim 1, wherein the custom ontology is received from the user and iscurated independently from the separate ontology.
 4. Thecomputer-implemented method of claim 1, wherein using the customontology to augment the input of the search query for the semanticsearch function comprises generating the suggestions associated with theinput of the search query.
 5. The computer-implemented method of claim1, wherein the custom user experience for searching the corpus comprisesgenerating the suggestions using the custom ontology.
 6. Thecomputer-implemented method of claim 1, wherein the custom userexperience for searching the corpus comprises generating the suggestionsusing the custom ontology and the separate ontology.
 7. Thecomputer-implemented method of claim 1, wherein the custom userexperience for searching the corpus comprises generating the suggestionsusing the custom ontology while avoiding execution of natural langueprocessing (NLP) on the corpus with the custom ontology.
 8. Thecomputer-implemented method of claim 1, wherein software is provided asa service in a cloud environment for providing the custom userexperience for searching the corpus using the custom ontology to augmentthe input of the search query.
 9. A system comprising: a memory havingcomputer readable instructions; and one or more processors for executingthe computer readable instructions, the computer readable instructionscontrolling the one or more processors to perform operations comprising:receiving, by a processor, a request from a user device, the requestcomprising a custom ontology as a structure having a plurality ofentities and relationships between the plurality of entities, whereinthe custom ontology having been received from the user device is to beassociated with a search query; in response to receiving the customontology transmitted from the user device, updating a semantic searchfunction with the custom ontology and a custom ontology index in placeof an original ontology, the semantic search function initiallysupporting the original ontology originally having been used to enrich acorpus, the custom ontology being separate and distinct from theoriginal ontology having enriched the corpus, wherein the corpuscomprises documents enriched by annotations in which the originalontology is used to generate the annotations, wherein natural languageprocessing uses the original ontology to generate the annotations suchthat the annotations are between co-occurring entities linked within theoriginal ontology, wherein the custom ontology index is configured toindex the custom ontology using a string match; in response to using thecustom ontology to replace the original ontology to augment input of thesearch query for the semantic search function to search the corpus,generating suggestions for the search query as a user is entering thesearch query by using the custom ontology and the custom ontology indexin place of the separate ontology for the suggestions such that thesuggestions displayed are from the custom ontology, thereby providing acustom user experience for searching the corpus; and searching thecorpus using the custom ontology, in response to the search query of thecustom ontology being mapped to the original ontology.
 10. The system ofclaim 9, wherein the custom ontology is different from the separateontology.
 11. The system of claim 9, wherein the custom ontology isreceived from the user and is curated independently from the separateontology.
 12. The system of claim 9, wherein using the custom ontologyto augment the input of the search query for the semantic searchfunction comprises generating the suggestions associated with the inputof the search query.
 13. The system of claim 9, wherein the custom userexperience for searching the corpus comprises generating the suggestionsusing the custom ontology.
 14. The system of claim 9, wherein the customuser experience for searching the corpus comprises generating thesuggestions using the custom ontology and the separate ontology.
 15. Thesystem of claim 9, wherein the custom user experience for searching thecorpus comprises generating the suggestions using the custom ontologywhile avoiding execution of natural langue processing (NLP) on thecorpus with the custom ontology.
 16. The system of claim 9, whereinsoftware is provided as a service in a cloud environment for providingthe custom user experience for searching the corpus using the customontology to augment the input of the search query.
 17. A computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to perform operationscomprising: receiving, by a processor, a request from a user device, therequest comprising a custom ontology as a structure having a pluralityof entities and relationships between the plurality of entities, whereinthe custom ontology having been received from the user device is to beassociated with a search query; in response to receiving the customontology transmitted from the user device, updating a semantic searchfunction with the custom ontology and a custom ontology index in placeof an original ontology, the semantic search function initiallysupporting the original ontology originally having been used to enrich acorpus, the custom ontology being separate and distinct from theoriginal ontology having enriched the corpus, wherein the corpuscomprises documents enriched by annotations in which the originalontology is used to generate the annotations, wherein natural languageprocessing uses the original ontology to generate the annotations suchthat the annotations are between co-occurring entities linked within theoriginal ontology, wherein the custom ontology index is configured toindex the custom ontology using a string match; in response to using thecustom ontology to replace the original ontology to augment input of thesearch query for the semantic search function to search the corpus,generating suggestions for the search query as a user is entering thesearch query by using the custom ontology and the custom ontology indexin place of the separate ontology for the suggestions such that thesuggestions displayed are from the custom ontology, thereby providing acustom user experience for searching the corpus; and searching thecorpus using the custom ontology, in response to the search query of thecustom ontology being mapped to the original ontology.
 18. The computerprogram product of claim 17, wherein the custom ontology is differentfrom the separate ontology.
 19. The computer program product of claim17, wherein the custom ontology is received from the user and is curatedindependently from the separate ontology.
 20. The computer-implementedmethod of claim 1, wherein the custom ontology index of the customontology is utilized to provide a look-up for the semantic searchfunction.